Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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1.080 Topics available

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977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

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PeopleLocationsStatistics
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Ruhr University Bochum

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (6/6 displayed)

  • 2023Transfer Learning Approaches In The Domain Of Radial-Axial Ring Rolling For Machine Learning Applicationscitations
  • 2023Transfer Learning Approaches In The Domain Of Radial-Axial Ring Rolling For Machine Learning Applicationscitations
  • 2023Complementary database generation for machine learning in quality prediction of cold ring rollingcitations
  • 2023Improvement of Machine Learning Models for Time Series Forecasting in Radial-Axial Ring Rolling through Transfer Learningcitations
  • 2023Identification Of Investigation Procedures To Predict Work Roll Fatigue For Developing Machine Learning Applications – A Systematic Literature Reviewcitations
  • 2023Identification Of Investigation Procedures To Predict Work Roll Fatigue For Developing Machine Learning Applications – A Systematic Literature Reviewcitations

Places of action

Chart of shared publication
Prinz, Christopher
2 / 2 shared
Fahle, Simon
2 / 7 shared
Kuhlenkötter, Bernd
6 / 14 shared
Moser, Tobias
5 / 7 shared
Hübner, Marco
2 / 6 shared
Herberger, David
2 / 6 shared
Lafarge, Rémi
1 / 7 shared
Brosius, Alexander
2 / 48 shared
Wang, Qinwen
2 / 2 shared
Alp, Enes
2 / 2 shared
Chart of publication period
2023

Co-Authors (by relevance)

  • Prinz, Christopher
  • Fahle, Simon
  • Kuhlenkötter, Bernd
  • Moser, Tobias
  • Hübner, Marco
  • Herberger, David
  • Lafarge, Rémi
  • Brosius, Alexander
  • Wang, Qinwen
  • Alp, Enes
OrganizationsLocationPeople